A Study on Federated Learning Techniques for Privacy Preservation

Abstract

The fast proliferation of data-driven applications and intelligent systems has raised problem awareness as far as the concerns regarding data privacy, data security, and regulatory compliance are concerned to a very high level. The conventional centralized machine learning models demand the coalescence of raw data situated in disseminated sources, which is a grave threat of information leaks, unauthorized data accessibility, and breaking a privacy policy like GDPR and HIPAA. Federated Learning (FL) has become a hopeful decentralized learning framework so as to facilitate joint model training among various customers without relocating crude data to a central point. Rather, model updates of the local models are shared and aggregated, hence retaining locality of data and improving privacy. This paper will provide an in-depth analysis of federated learning methods and pay special attention to the issue of privacy. Its research paper investigates the very principles of federated learning, architecture designs, communication scheme, and ways of aggregation. The diverse privacy-enhancing schemes are secure aggregation, differential privacy, homomorphic encryption, trusted execution environments, and are critically analyzed. Moreover, this article examines new developments, concerns and tradeoffs connected with privacy, effectiveness of communication, model noise, and scalability of systems. There is a comparative analysis of federated learning methods presented in organized tabular form and mathematical equations. The existing studies are discussed with their experimental results in order to outline the effectiveness of federated learning to maintain the privacy and achieve the acceptable model accuracy. Lastly, the research issues and future paths are outlined in order to direct the further development of the privacy-preserving federated learning systems.